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The costly and time-consuming determination of thermophysical properties of nanofluids through the experimental analysis leads the current investigations to use the soft computing methods like correlating, artificial neural network (ANN) and genetic algorithm. In this study, the application of ANN, empirical correlations and genetic algorithm for modeling and multi-criteria optimization of the thermophysical properties of clove-treated MWCNTs nanofluid which has been synthesized through a facile and eco-friendly procedure has been investigated. In this contribution, totally 6 structures are assessed: networks including one and two hidden layers with 2, 4, and 6 neurons. From assessment of the ANN, it is found that the network including two hidden layers with 4 neurons in every layer results in the least difference between the network outputs and the experimental data, providing the best performance. It is concluded that the optimal ANN model is a more precise and accurate way to predict the thermal conductivity and viscosity of environmentally friendly C-MWCNT/water nanofluid compared to empirical correlations obtained from non-linear regression method. Moreover, the evolutionary algorithm has been implemented for achieving the optimal conditions to maximize the thermal conductivity and to minimize the viscosity of nanofluid. In this regard, based on real-world engineering experience, the final optimal solutions opted from several distinguished procedures of decision-making including the Bellman-Zadeh, TOPSIS and LINMAP approaches has been investigated in parallel. The results of this study revealed that the obtained outputs using TOPSIS and LINMAP procedures are the closest to ideal solution. [ABSTRACT FROM AUTHOR]

In this paper, an experimental evaluation on the viscosity of water-based manganese ferrite nanofluid with and without magnetic field with 100, 200, 300, and 400 G intensities has been conducted. The Brookfield DV-I PRIME viscometer is implemented to measure the MnFe 2 O 4 /water nanofluid viscosity and to evaluate the influence of different volume concentrations (from 0.25% to 3%) and various temperatures (from 20 to 60 °C) on the viscosity. According to the measurements, viscosity incrementally increases with the augmentation of nanoparticles concentration while it remarkably decreases at higher temperatures under absence and attendance of magnetic field. The maximum viscosity ratio of 1.14 is achieved at 3 vol% of nanoparticles and 20 °C under no magnetic field, whereas it increments to maximum viscosity ratio of 1.75 at 3 vol% of nanoparticles and 40 °C under 400 G magnetic field. Furthermore, new correlation is proposed for determination of viscosity of MnFe 2 O 4 /water nanofluids in terms of magnetic field intensity, volume concentration and temperature. [ABSTRACT FROM AUTHOR]

In this study, the rheological behavior and dynamic viscosity of 5 W30 engine oil based ZnO-MWCNT (30:70) hybrid nanofluid with various volume fractions (VFs) ranging from 0.05 to 1 vol%, temperatures in the range of 5–55 °C, and shear rates altering from 50 to 1000 rpm are experimentally evaluated. The measured viscosity values at different shear rates (SRs) and temperatures revealed that the hybrid nanofluid under study has a non-Newtonian behavior. For example, with increasing SR from 50 to 300 rpm, the maximum viscosity reduction (= 25.6%) occurs at temperature of 5 °C and VF of 1%, while with increasing SR from 700 to 1000 rpm, the minimum viscosity reduction (= 7.6%) occurs at temperature of 55 °C and VF of 0.05%. Moreover, the power law index is observed less than unity indicating pseudoplastic behavior of hybrid nanofluid under study in all VFs and temperatures. The experimental results show that the increase of the VF of nanoparticles leads to the elevated viscosity. By raising the nanofluid temperature, the viscosity of hybrid nanofluid reduces, while engine oil without additives has a high viscosity at elevated temperatures. As an example, considering constant SR of 300 rpm, the increase of temperature from 5 to 55 °C leads to the 78.4% and 85.5% viscosity reduction for the hybrid nanofluid with VF of 0.5% and 0.75%, respectively. In order to predict the experimental data, a three-variable correlation (depending on the temperature, VF of nanoparticles, and SR) and artificial neural network modeling are developed. It is concluded that an excellent agreement exists between the experimental data, correlation results, and predicted data by neural network, however, neural network modeling demonstrated its capability to estimate the viscosity of 5 W30 engine oil based ZnO-MWCNT hybrid nanofluid more precisely rather than the proposed correlation. Hence, the neural network modeling is highly recommended for prediction of viscosity of the hybrid nanofluid under study. [Display omitted] • Rheological behavior of ZnO-MWCNT (30:70)/5 W30 hybrid nanofluid is evaluated. • Effects of concentration, temperature, and shear rates on the viscosity is studied. • A three-variable correlation for viscosity estimation is developed. • Comparison of correlation results with those of available correlations is done. • A neural network modeling is developed for predicting the experimental data. [ABSTRACT FROM AUTHOR]